Human capital is suggested as a significant determinant of economic growth in the context of new economic growth theories. Education is a significant factor underlying human capital. Therefore, this study analyzes the reciprocal interplay between higher education and economic growth in sample of the new EU member states over the 1995-2020 term through causality analysis. The results of the causality test reveal a one-way causal relationship from economic development to higher education in the new EU member states.
TopIntroduction
Sustainable economic growth and fair income distribution is vital for economic development, but there does not exist a single economic growth model which fits all economies. However, human capital and knowledge accumulation are among main determinants of economic growth. Physical capital is also important for economic growth, but it has a secondary role in economic growth (Lucas, 1988). Harmonization of physical capital and human capital is seen as a measure of economic growth and contemporary economic development (Dikmen, 2006).
Education level is accepted as an exogenous factor affecting the labor in Solow-Swan growth model (Solow, 1956 and Swan, 1956). The Solow-Swan growth model suggests that improvements in education can affect the economic growth through increases in labor quality and productivity (Solow, 1956 and Swan, 1956). Therefore, governments should follow an education policy which enables the society to reach the contemporary knowledge level and increase the on-the-job training activities for sustainable economic growth. In this regard, education is a critical for future of individuals and societies (Dikmen, 2006).
The significance of education and human capital in the theories of economic growth was particularly emphasized by endogenous growth models and the extended neoclassical growth model of Mankiw, Romer, and Weil (MRW) as of 1980s. (Mankiw, Romer and Weil, 1992). The extended neoclassical growth model takes the human capital as an additional input to production function and in turn countries with higher improvement in education will experience relatively higher growth rates and higher incomes (Romer, 1990).
The endogenous growth models view the education as a process which changes the production technology through new products, processes, or knowledge and makes the adaptation of foreign technologies easier or facilitates the resource transfer to the most technologically dynamic sector of the economy (Gyimah-Brempong et al., 2006). In the endogenous growth theories, education is accepted as subject to increasing returns in order to overcome the growth-reducing effects of decreasing returns on physical capital (Romer, 1986; Lucas, 1988). Both endogenous and extended neoclassical growth models suggest that education has a positive impact on income growth rate. However, a threshold of education level is required to achieve the positive growth effect of education (Gyimah-Brempong et al., 2006). In the empirical studies, the scholars have generally investigated the impact of education, education investments, and educational expenditures on economic growth and development as seen in the literature review section. Therefore, this chapter investigates the reciprocal interplay between higher education and economic growth in the new EU (European Union) member economies over the 1995-2020 period through causality test with cross-sectional dependence taking the gap in the empirical literature into account. The new EU member states have achieved a remarkable improvement in real GDP per capita and tertiary school enrollment as seen in Table 1 as of transition and EU membership processes. For this reason, we investigate the interplay between tertiary school enrollment and real GDP per capita in sample of the new EU member states.
Table 1. Real GDP and tertiary school enrollments in the new EU members
Countries | Year | Real GDP per capita based on constant 2015 US$ | Tertiary school enrollment (% of total enrollment) |
Bulgaria | 1995 | 4021.965 | 36.40219 |
2020 | 7956.469 | 75.4058 |
Croatia | 1995 | 7226.353 | 26.23511 |
2020 | 12920.34 | 68.09648 |
Czechia | 1995 | 11219.15 | 20.57965 |
2020 | 19048.09 | 68.06181 |
Estonia | 1995 | 7137.531 | 25.44992 |
2020 | 20118.11 | 75.123 |
Hungary | 1995 | 7675.578 | 22.15632 |
2020 | 14427.82 | 55.15554 |
Latvia | 1995 | 4969.823 | 22.76615 |
2020 | 15826.41 | 94.51083 |
Lithuania | 1995 | 4936.02 | 26.29737 |
2020 | 17241.35 | 70.7869 |
Poland | 1995 | 5628.446 | 31.17898 |
2020 | 14774.99 | 70.47713 |
Romania | 1995 | 4570.147 | 13.38848 |
2020 | 10898.83 | 53.23084 |
Slovakia | 1995 | 7542.111 | 18.55095 |
2020 | 17611.76 | 47.62359 |
Slovenia | 1995 | 13276.07 | 30.39881 |
2020 | 22928.76 | 79.92084 |
Source: World Bank (2023a&2023b)